Minh N. Vu

Orcid: 0000-0001-8727-0350

According to our database1, Minh N. Vu authored at least 20 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
LLM-assisted Concept Discovery: Automatically Identifying and Explaining Neuron Functions.
CoRR, 2024

CHARME: A chain-based reinforcement learning approach for the minor embedding problem.
CoRR, 2024

Analysis of Privacy Leakage in Federated Large Language Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Limitations of Perturbation-based Explanation Methods for Temporal Graph Neural Networks.
Proceedings of the IEEE International Conference on Data Mining, 2023

Active Data Reconstruction Attacks in Vertical Federated Learning.
Proceedings of the IEEE International Conference on Big Data, 2023

2022
On the Limit of Explaining Black-box Temporal Graph Neural Networks.
CoRR, 2022

EMaP: Explainable AI with Manifold-based Perturbations.
CoRR, 2022

NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee.
CoRR, 2022

NeuCEPT: Learn Neural Networks' Mechanism via Critical Neurons with Precision Guarantee.
Proceedings of the IEEE International Conference on Data Mining, 2022

An Explainer for Temporal Graph Neural Networks.
Proceedings of the IEEE Global Communications Conference, 2022

2021
Evaluating Fake News Detection Models from Explainable Machine Learning Perspectives.
Proceedings of the ICC 2021, 2021

c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Learning Interpretation with Explainable Knowledge Distillation.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
Capacity Region and Capacity-Achieving Signaling Schemes for 1-bit ADC Multiple Access Channels in Rayleigh Fading.
IEEE Trans. Wirel. Commun., 2020

PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Optimal Signaling Schemes and Capacity of Non-Coherent Rician Fading Channels With Low-Resolution Output Quantization.
IEEE Trans. Wirel. Commun., 2019

Optimal Signaling Schemes and Capacities of Non-Coherent Correlated MISO Channels Under Per-Antenna Power Constraints.
IEEE Trans. Commun., 2019

Evaluating Explainers via Perturbation.
CoRR, 2019

Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018
Optimal Signaling Scheme and Capacity of Non-Coherent Rician Fading Channels with 1-Bit Output Quantization.
Proceedings of the 2018 IEEE International Conference on Communications, 2018


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